Overview

Brought to you by YData

Dataset statistics

Number of variables41
Number of observations79542
Missing cells467279
Missing cells (%)14.3%
Total size in memory24.9 MiB
Average record size in memory328.0 B

Variable types

Numeric24
Text16
Unsupported1

Alerts

RUNDATE has constant value "45401.0" Constant
ACQDATE has 35323 (44.4%) missing values Missing
ADDRESS2 has 74814 (94.1%) missing values Missing
CBSA has 20151 (25.3%) missing values Missing
CBSA_DIV has 64418 (81.0%) missing values Missing
CBSA_DIV_NO has 44268 (55.7%) missing values Missing
CBSA_METRO_NAME has 28187 (35.4%) missing values Missing
CSA has 31357 (39.4%) missing values Missing
CSA_NO has 11207 (14.1%) missing values Missing
MDI_STATUS_CODE has 77963 (98.0%) missing values Missing
MDI_STATUS_DESC has 77963 (98.0%) missing values Missing
STALP has 803 (1.0%) missing values Missing
STNAME has 803 (1.0%) missing values Missing
OBJECTID has unique values Unique
MDI_STATUS_CODE is an unsupported type, check if it needs cleaning or further analysis Unsupported
X has 5766 (7.2%) zeros Zeros
Y has 5766 (7.2%) zeros Zeros
CBSA_DIV_FLG has 64418 (81.0%) zeros Zeros
CBSA_DIV_NO has 20151 (25.3%) zeros Zeros
CBSA_METRO has 28187 (35.4%) zeros Zeros
CBSA_METRO_FLG has 28187 (35.4%) zeros Zeros
CBSA_MICRO_FLG has 71505 (89.9%) zeros Zeros
CBSA_NO has 20151 (25.3%) zeros Zeros
CSA_FLG has 31357 (39.4%) zeros Zeros
CSA_NO has 20151 (25.3%) zeros Zeros
LATITUDE has 5766 (7.2%) zeros Zeros
LONGITUDE has 5766 (7.2%) zeros Zeros
MAINOFF has 74964 (94.2%) zeros Zeros
OFFNUM has 4577 (5.8%) zeros Zeros
STCNTY has 803 (1.0%) zeros Zeros

Reproduction

Analysis started2025-06-03 16:34:33.317255
Analysis finished2025-06-03 16:34:39.452373
Duration6.14 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

X
Real number (ℝ)

Zeros 

Distinct72672
Distinct (%)91.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-83.77563019
Minimum-166.2485582
Maximum163.01083
Zeros5766
Zeros (%)7.2%
Negative73745
Negative (%)92.7%
Memory size621.6 KiB
2025-06-03T16:34:39.588339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-166.2485582
5-th percentile-121.1655976
Q1-95.93160797
median-85.90068998
Q3-77.36972078
95-th percentile0
Maximum163.01083
Range329.2593882
Interquartile range (IQR)18.56188719

Descriptive statistics

Standard deviation27.77400847
Coefficient of variation (CV)-0.3315284935
Kurtosis5.258144479
Mean-83.77563019
Median Absolute Deviation (MAD)9.340964979
Skewness1.848770714
Sum-6663597.401
Variance771.3955462
MonotonicityNot monotonic
2025-06-03T16:34:39.754424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5766
 
7.2%
-92.296582 11
 
< 0.1%
-112.0348945 9
 
< 0.1%
-66.059463 8
 
< 0.1%
-66.028075 8
 
< 0.1%
-93.27205096 6
 
< 0.1%
-98.495664 6
 
< 0.1%
-66.625687 6
 
< 0.1%
-96.80482098 6
 
< 0.1%
-85.49964299 5
 
< 0.1%
Other values (72662) 73710
92.7%
ValueCountFrequency (%)
-166.2485582 1
< 0.1%
-165.4092107 1
< 0.1%
-165.4082716 1
< 0.1%
-162.592615 1
< 0.1%
-161.759877 1
< 0.1%
ValueCountFrequency (%)
163.01083 1
< 0.1%
158.2056 2
< 0.1%
151.84316 1
< 0.1%
151.84113 1
< 0.1%
145.760035 1
< 0.1%

Y
Real number (ℝ)

Zeros 

Distinct72636
Distinct (%)91.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean35.08090051
Minimum-24.89278582
Maximum71.293282
Zeros5766
Zeros (%)7.2%
Negative1
Negative (%)< 0.1%
Memory size621.6 KiB
2025-06-03T16:34:39.930292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-24.89278582
5-th percentile0
Q133.44270901
median38.32520001
Q341.24813555
95-th percentile44.986226
Maximum71.293282
Range96.18606782
Interquartile range (IQR)7.805426545

Descriptive statistics

Standard deviation11.03089641
Coefficient of variation (CV)0.3144416548
Kurtosis4.645609341
Mean35.08090051
Median Absolute Deviation (MAD)3.797493988
Skewness-2.211858374
Sum2790369.908
Variance121.6806756
MonotonicityNot monotonic
2025-06-03T16:34:40.075438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5766
 
7.2%
38.97345401 11
 
< 0.1%
33.50985786 9
 
< 0.1%
18.347484 8
 
< 0.1%
18.210621 8
 
< 0.1%
29.42687902 6
 
< 0.1%
32.86271798 6
 
< 0.1%
44.97609999 6
 
< 0.1%
18.103866 6
 
< 0.1%
43.08686201 5
 
< 0.1%
Other values (72626) 73710
92.7%
ValueCountFrequency (%)
-24.89278582 1
 
< 0.1%
0 5766
7.2%
5.32492 1
 
< 0.1%
6.964 2
 
< 0.1%
7.34391 1
 
< 0.1%
ValueCountFrequency (%)
71.293282 1
< 0.1%
66.892278 1
< 0.1%
66.56444 1
< 0.1%
64.85844899 1
< 0.1%
64.85833001 1
< 0.1%

OBJECTID
Real number (ℝ)

Unique 

Distinct79542
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39771.5
Minimum1
Maximum79542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:40.232616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3978.05
Q119886.25
median39771.5
Q359656.75
95-th percentile75564.95
Maximum79542
Range79541
Interquartile range (IQR)39770.5

Descriptive statistics

Standard deviation22961.94189
Coefficient of variation (CV)0.57734664
Kurtosis-1.2
Mean39771.5
Median Absolute Deviation (MAD)19885.5
Skewness0
Sum3163504653
Variance527250775.5
MonotonicityStrictly increasing
2025-06-03T16:34:40.385256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79542 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
79526 1
 
< 0.1%
79525 1
 
< 0.1%
79524 1
 
< 0.1%
79523 1
 
< 0.1%
Other values (79532) 79532
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
ValueCountFrequency (%)
79542 1
< 0.1%
79541 1
< 0.1%
79540 1
< 0.1%
79539 1
< 0.1%
79538 1
< 0.1%

ACQDATE
Real number (ℝ)

Missing 

Distinct3688
Distinct (%)8.3%
Missing35323
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean39808.90025
Minimum25658
Maximum45383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:40.539277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25658
5-th percentile33798
Q137520.5
median40039
Q342651
95-th percentile44658
Maximum45383
Range19725
Interquartile range (IQR)5130.5

Descriptive statistics

Standard deviation3412.575657
Coefficient of variation (CV)0.085723937
Kurtosis-0.05132300745
Mean39808.90025
Median Absolute Deviation (MAD)2604
Skewness-0.4543849941
Sum1760309760
Variance11645672.61
MonotonicityNot monotonic
2025-06-03T16:34:40.691057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40257 2063
 
2.6%
36364 1127
 
1.4%
38304 1123
 
1.4%
39716 1115
 
1.4%
40123 763
 
1.0%
38516 759
 
1.0%
43806 745
 
0.9%
37112 646
 
0.8%
40086 632
 
0.8%
44477 539
 
0.7%
Other values (3678) 34707
43.6%
(Missing) 35323
44.4%
ValueCountFrequency (%)
25658 1
 
< 0.1%
25717 1
 
< 0.1%
25720 2
< 0.1%
25750 4
< 0.1%
25837 1
 
< 0.1%
ValueCountFrequency (%)
45383 67
0.1%
45370 5
 
< 0.1%
45352 14
 
< 0.1%
45344 1
 
< 0.1%
45339 6
 
< 0.1%

ADDRESS
Text

Distinct74294
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:41.130951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length73
Median length65
Mean length16.66408941
Min length3

Characters and Unicode

Total characters1325495
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71617 ?
Unique (%)90.0%

Sample

1st row18001 Saint Rose Rd
2nd row1350 12th St
3rd row500 W Harrison St
4th row891 Fairfax St
5th row240 Salt Lick Rd
ValueCountFrequency (%)
st 24493
 
8.4%
ave 12833
 
4.4%
rd 12463
 
4.3%
n 8040
 
2.8%
w 7978
 
2.7%
s 7933
 
2.7%
main 7559
 
2.6%
e 7123
 
2.4%
blvd 6243
 
2.1%
dr 4518
 
1.5%
Other values (24052) 192899
66.0%
2025-06-03T16:34:41.719307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
212625
 
16.0%
e 72758
 
5.5%
t 63820
 
4.8%
1 60682
 
4.6%
a 59845
 
4.5%
0 55690
 
4.2%
n 47693
 
3.6%
r 45183
 
3.4%
S 43994
 
3.3%
i 41606
 
3.1%
Other values (65) 621599
46.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1325495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
212625
 
16.0%
e 72758
 
5.5%
t 63820
 
4.8%
1 60682
 
4.6%
a 59845
 
4.5%
0 55690
 
4.2%
n 47693
 
3.6%
r 45183
 
3.4%
S 43994
 
3.3%
i 41606
 
3.1%
Other values (65) 621599
46.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1325495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
212625
 
16.0%
e 72758
 
5.5%
t 63820
 
4.8%
1 60682
 
4.6%
a 59845
 
4.5%
0 55690
 
4.2%
n 47693
 
3.6%
r 45183
 
3.4%
S 43994
 
3.3%
i 41606
 
3.1%
Other values (65) 621599
46.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1325495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
212625
 
16.0%
e 72758
 
5.5%
t 63820
 
4.8%
1 60682
 
4.6%
a 59845
 
4.5%
0 55690
 
4.2%
n 47693
 
3.6%
r 45183
 
3.4%
S 43994
 
3.3%
i 41606
 
3.1%
Other values (65) 621599
46.9%

ADDRESS2
Text

Missing 

Distinct1302
Distinct (%)27.5%
Missing74814
Missing (%)94.1%
Memory size621.6 KiB
2025-06-03T16:34:42.120840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length51
Median length7
Mean length7.442681895
Min length1

Characters and Unicode

Total characters35189
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique979 ?
Unique (%)20.7%

Sample

1st rowSte 104
2nd rowSte 150
3rd rowSte 300
4th rowSte D
5th rowSte 105
ValueCountFrequency (%)
ste 3691
37.3%
100 858
 
8.7%
101 275
 
2.8%
suite 262
 
2.6%
a 260
 
2.6%
223
 
2.3%
200 135
 
1.4%
unit 131
 
1.3%
1 127
 
1.3%
150 108
 
1.1%
Other values (1143) 3832
38.7%
2025-06-03T16:34:42.666941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5180
14.7%
0 4420
12.6%
t 4343
12.3%
e 4331
12.3%
S 4064
11.5%
1 3452
9.8%
2 1036
 
2.9%
5 653
 
1.9%
i 604
 
1.7%
3 530
 
1.5%
Other values (62) 6576
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35189
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5180
14.7%
0 4420
12.6%
t 4343
12.3%
e 4331
12.3%
S 4064
11.5%
1 3452
9.8%
2 1036
 
2.9%
5 653
 
1.9%
i 604
 
1.7%
3 530
 
1.5%
Other values (62) 6576
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35189
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5180
14.7%
0 4420
12.6%
t 4343
12.3%
e 4331
12.3%
S 4064
11.5%
1 3452
9.8%
2 1036
 
2.9%
5 653
 
1.9%
i 604
 
1.7%
3 530
 
1.5%
Other values (62) 6576
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35189
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5180
14.7%
0 4420
12.6%
t 4343
12.3%
e 4331
12.3%
S 4064
11.5%
1 3452
9.8%
2 1036
 
2.9%
5 653
 
1.9%
i 604
 
1.7%
3 530
 
1.5%
Other values (62) 6576
18.7%

BKCLASS
Text

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:42.769880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length2
Mean length1.537954791
Min length1

Characters and Unicode

Total characters122332
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSM
2nd rowSM
3rd rowSM
4th rowSM
5th rowNM
ValueCountFrequency (%)
n 36757
46.2%
nm 25032
31.5%
sm 13308
 
16.7%
si 2446
 
3.1%
sb 1842
 
2.3%
sl 146
 
0.2%
oi 10
 
< 0.1%
bkclass 1
 
< 0.1%
2025-06-03T16:34:42.964569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 61789
50.5%
M 38340
31.3%
S 17744
 
14.5%
I 2456
 
2.0%
B 1843
 
1.5%
L 147
 
0.1%
O 10
 
< 0.1%
K 1
 
< 0.1%
C 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 61789
50.5%
M 38340
31.3%
S 17744
 
14.5%
I 2456
 
2.0%
B 1843
 
1.5%
L 147
 
0.1%
O 10
 
< 0.1%
K 1
 
< 0.1%
C 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 61789
50.5%
M 38340
31.3%
S 17744
 
14.5%
I 2456
 
2.0%
B 1843
 
1.5%
L 147
 
0.1%
O 10
 
< 0.1%
K 1
 
< 0.1%
C 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 61789
50.5%
M 38340
31.3%
S 17744
 
14.5%
I 2456
 
2.0%
B 1843
 
1.5%
L 147
 
0.1%
O 10
 
< 0.1%
K 1
 
< 0.1%
C 1
 
< 0.1%
A 1
 
< 0.1%

CBSA
Text

Missing 

Distinct823
Distinct (%)1.4%
Missing20151
Missing (%)25.3%
Memory size621.6 KiB
2025-06-03T16:34:43.211758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length34
Mean length24.39625532
Min length4

Characters and Unicode

Total characters1448918
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowSt. Louis, MO-IL
2nd rowSt. Louis, MO-IL
3rd rowSt. Louis, MO-IL
4th rowSt. Louis, MO-IL
5th rowSt. Louis, MO-IL
ValueCountFrequency (%)
city 6526
 
4.3%
tx 5732
 
3.8%
new 4694
 
3.1%
york-newark-jersey 4299
 
2.8%
ny-nj-pa 4299
 
2.8%
fl 4287
 
2.8%
oh 2334
 
1.5%
il-in-wi 2252
 
1.5%
chicago-naperville-elgin 2252
 
1.5%
pa 2151
 
1.4%
Other values (922) 113084
74.4%
2025-06-03T16:34:43.593472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 94130
 
6.5%
- 92785
 
6.4%
92519
 
6.4%
e 91360
 
6.3%
o 78746
 
5.4%
n 76660
 
5.3%
r 71410
 
4.9%
i 65973
 
4.6%
l 61528
 
4.2%
, 59390
 
4.1%
Other values (47) 664417
45.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1448918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 94130
 
6.5%
- 92785
 
6.4%
92519
 
6.4%
e 91360
 
6.3%
o 78746
 
5.4%
n 76660
 
5.3%
r 71410
 
4.9%
i 65973
 
4.6%
l 61528
 
4.2%
, 59390
 
4.1%
Other values (47) 664417
45.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1448918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 94130
 
6.5%
- 92785
 
6.4%
92519
 
6.4%
e 91360
 
6.3%
o 78746
 
5.4%
n 76660
 
5.3%
r 71410
 
4.9%
i 65973
 
4.6%
l 61528
 
4.2%
, 59390
 
4.1%
Other values (47) 664417
45.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1448918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 94130
 
6.5%
- 92785
 
6.4%
92519
 
6.4%
e 91360
 
6.3%
o 78746
 
5.4%
n 76660
 
5.3%
r 71410
 
4.9%
i 65973
 
4.6%
l 61528
 
4.2%
, 59390
 
4.1%
Other values (47) 664417
45.9%

CBSA_DIV
Text

Missing 

Distinct27
Distinct (%)0.2%
Missing64418
Missing (%)81.0%
Memory size621.6 KiB
2025-06-03T16:34:43.826002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length38
Mean length30.63343031
Min length8

Characters and Unicode

Total characters463300
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCamden, NJ
2nd rowCamden, NJ
3rd rowMontgomery County-Bucks County-Chester County, PA
4th rowCamden, NJ
5th rowChicago-Naperville-Evanston, IL
ValueCountFrequency (%)
new 3040
 
6.6%
york-jersey 2392
 
5.2%
city-white 2392
 
5.2%
plains 2392
 
5.2%
ny-nj 2392
 
5.2%
il 1863
 
4.1%
chicago-naperville-evanston 1679
 
3.7%
tx 1646
 
3.6%
county 1555
 
3.4%
fl 1350
 
3.0%
Other values (51) 25026
54.7%
2025-06-03T16:34:44.212840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 30816
 
6.7%
30603
 
6.6%
- 30147
 
6.5%
a 29318
 
6.3%
n 27591
 
6.0%
o 24817
 
5.4%
i 24424
 
5.3%
t 21093
 
4.6%
r 20954
 
4.5%
l 18764
 
4.1%
Other values (39) 204773
44.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 463300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 30816
 
6.7%
30603
 
6.6%
- 30147
 
6.5%
a 29318
 
6.3%
n 27591
 
6.0%
o 24817
 
5.4%
i 24424
 
5.3%
t 21093
 
4.6%
r 20954
 
4.5%
l 18764
 
4.1%
Other values (39) 204773
44.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 463300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 30816
 
6.7%
30603
 
6.6%
- 30147
 
6.5%
a 29318
 
6.3%
n 27591
 
6.0%
o 24817
 
5.4%
i 24424
 
5.3%
t 21093
 
4.6%
r 20954
 
4.5%
l 18764
 
4.1%
Other values (39) 204773
44.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 463300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 30816
 
6.7%
30603
 
6.6%
- 30147
 
6.5%
a 29318
 
6.3%
n 27591
 
6.0%
o 24817
 
5.4%
i 24424
 
5.3%
t 21093
 
4.6%
r 20954
 
4.5%
l 18764
 
4.1%
Other values (39) 204773
44.2%

CBSA_DIV_FLG
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.1901283615
Minimum0
Maximum1
Zeros64418
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:44.301979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39240477
Coefficient of variation (CV)2.063893924
Kurtosis0.4944746655
Mean0.1901283615
Median Absolute Deviation (MAD)0
Skewness1.579386664
Sum15123
Variance0.1539815035
MonotonicityNot monotonic
2025-06-03T16:34:44.388206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 64418
81.0%
1 15123
 
19.0%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 64418
81.0%
1 15123
 
19.0%
ValueCountFrequency (%)
1 15123
 
19.0%
0 64418
81.0%

CBSA_DIV_NO
Real number (ℝ)

Missing  Zeros 

Distinct27
Distinct (%)0.1%
Missing44268
Missing (%)55.7%
Infinite0
Infinite (%)0.0%
Mean13093.45841
Minimum0
Maximum48864
Zeros20151
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:44.851296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q329404
95-th percentile47664
Maximum48864
Range48864
Interquartile range (IQR)29404

Descriptive statistics

Standard deviation16743.61604
Coefficient of variation (CV)1.278777196
Kurtosis-0.9088239627
Mean13093.45841
Median Absolute Deviation (MAD)0
Skewness0.796928153
Sum461858652
Variance280348678
MonotonicityNot monotonic
2025-06-03T16:34:44.963686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 20151
25.3%
35614 2392
 
3.0%
16984 1679
 
2.1%
19124 1129
 
1.4%
47894 951
 
1.2%
15764 694
 
0.9%
35004 657
 
0.8%
35154 648
 
0.8%
42644 613
 
0.8%
35084 602
 
0.8%
Other values (17) 5758
 
7.2%
(Missing) 44268
55.7%
ValueCountFrequency (%)
0 20151
25.3%
14454 575
 
0.7%
15764 694
 
0.9%
15804 262
 
0.3%
16984 1679
 
2.1%
ValueCountFrequency (%)
48864 199
 
0.3%
48424 396
0.5%
47894 951
1.2%
47664 532
0.7%
45104 127
 
0.2%

CBSA_METRO
Real number (ℝ)

Zeros 

Distinct330
Distinct (%)0.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean19092.77002
Minimum0
Maximum49660
Zeros28187
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:45.105935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17460
Q335620
95-th percentile45500
Maximum49660
Range49660
Interquartile range (IQR)35620

Descriptive statistics

Standard deviation16729.76215
Coefficient of variation (CV)0.8762354616
Kurtosis-1.439491546
Mean19092.77002
Median Absolute Deviation (MAD)17460
Skewness0.1533729248
Sum1518658020
Variance279884941.6
MonotonicityNot monotonic
2025-06-03T16:34:45.248566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28187
35.4%
35620 4299
 
5.4%
16980 2252
 
2.8%
19100 1646
 
2.1%
37980 1427
 
1.8%
14460 1387
 
1.7%
26420 1360
 
1.7%
33100 1350
 
1.7%
47900 1238
 
1.6%
12060 1071
 
1.3%
Other values (320) 35324
44.4%
ValueCountFrequency (%)
0 28187
35.4%
10180 47
 
0.1%
10380 15
 
< 0.1%
10420 165
 
0.2%
10500 37
 
< 0.1%
ValueCountFrequency (%)
49660 117
0.1%
49620 102
0.1%
49500 7
 
< 0.1%
49420 39
 
< 0.1%
49340 196
0.2%

CBSA_METRO_FLG
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.6456292981
Minimum0
Maximum1
Zeros28187
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:45.352945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4783251864
Coefficient of variation (CV)0.7408666054
Kurtosis-1.629246841
Mean0.6456292981
Median Absolute Deviation (MAD)0
Skewness-0.6089286705
Sum51354
Variance0.228794984
MonotonicityNot monotonic
2025-06-03T16:34:45.435071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 51354
64.6%
0 28187
35.4%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 28187
35.4%
1 51354
64.6%
ValueCountFrequency (%)
1 51354
64.6%
0 28187
35.4%

CBSA_METRO_NAME
Text

Missing 

Distinct330
Distinct (%)0.6%
Missing28187
Missing (%)35.4%
Memory size621.6 KiB
2025-06-03T16:34:45.648535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length33
Mean length26.13657872
Min length8

Characters and Unicode

Total characters1342244
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSt. Louis, MO-IL
2nd rowSt. Louis, MO-IL
3rd rowSt. Louis, MO-IL
4th rowSt. Louis, MO-IL
5th rowSt. Louis, MO-IL
ValueCountFrequency (%)
city 6268
 
4.7%
tx 5237
 
3.9%
new 4619
 
3.4%
york-newark-jersey 4299
 
3.2%
ny-nj-pa 4299
 
3.2%
fl 4213
 
3.1%
il-in-wi 2252
 
1.7%
chicago-naperville-elgin 2252
 
1.7%
pa 1815
 
1.4%
oh 1734
 
1.3%
Other values (460) 97386
72.5%
2025-06-03T16:34:46.187043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 91806
 
6.8%
a 88095
 
6.6%
e 84320
 
6.3%
83019
 
6.2%
o 73512
 
5.5%
n 70904
 
5.3%
r 66684
 
5.0%
i 61287
 
4.6%
l 56478
 
4.2%
t 54828
 
4.1%
Other values (45) 611311
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1342244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 91806
 
6.8%
a 88095
 
6.6%
e 84320
 
6.3%
83019
 
6.2%
o 73512
 
5.5%
n 70904
 
5.3%
r 66684
 
5.0%
i 61287
 
4.6%
l 56478
 
4.2%
t 54828
 
4.1%
Other values (45) 611311
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1342244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 91806
 
6.8%
a 88095
 
6.6%
e 84320
 
6.3%
83019
 
6.2%
o 73512
 
5.5%
n 70904
 
5.3%
r 66684
 
5.0%
i 61287
 
4.6%
l 56478
 
4.2%
t 54828
 
4.1%
Other values (45) 611311
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1342244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 91806
 
6.8%
a 88095
 
6.6%
e 84320
 
6.3%
83019
 
6.2%
o 73512
 
5.5%
n 70904
 
5.3%
r 66684
 
5.0%
i 61287
 
4.6%
l 56478
 
4.2%
t 54828
 
4.1%
Other values (45) 611311
45.5%

CBSA_MICRO_FLG
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.1010296577
Minimum0
Maximum1
Zeros71505
Zeros (%)89.9%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:46.326013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3013698853
Coefficient of variation (CV)2.982984327
Kurtosis5.010857785
Mean0.1010296577
Median Absolute Deviation (MAD)0
Skewness2.647778652
Sum8036
Variance0.09082380778
MonotonicityNot monotonic
2025-06-03T16:34:46.450109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 71505
89.9%
1 8036
 
10.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 71505
89.9%
1 8036
 
10.1%
ValueCountFrequency (%)
1 8036
 
10.1%
0 71505
89.9%

CBSA_NO
Real number (ℝ)

Zeros 

Distinct823
Distinct (%)1.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean22142.5745
Minimum0
Maximum49820
Zeros20151
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:46.626592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22800
Q335620
95-th percentile46140
Maximum49820
Range49820
Interquartile range (IQR)35620

Descriptive statistics

Standard deviation16085.45807
Coefficient of variation (CV)0.7264493149
Kurtosis-1.313393349
Mean22142.5745
Median Absolute Deviation (MAD)12820
Skewness-0.1023124957
Sum1761242518
Variance258741961.4
MonotonicityNot monotonic
2025-06-03T16:34:46.829142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20151
25.3%
35620 4299
 
5.4%
16980 2252
 
2.8%
19100 1646
 
2.1%
37980 1427
 
1.8%
14460 1387
 
1.7%
26420 1360
 
1.7%
33100 1350
 
1.7%
47900 1238
 
1.6%
12060 1071
 
1.3%
Other values (813) 43360
54.5%
ValueCountFrequency (%)
0 20151
25.3%
10100 20
 
< 0.1%
10140 23
 
< 0.1%
10180 47
 
0.1%
10220 15
 
< 0.1%
ValueCountFrequency (%)
49820 3
 
< 0.1%
49780 27
 
< 0.1%
49660 117
0.1%
49620 102
0.1%
49500 7
 
< 0.1%

CERT
Real number (ℝ)

Distinct4577
Distinct (%)5.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14436.78109
Minimum14
Maximum91325
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:47.017598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile628
Q13511
median9712
Q318609
95-th percentile57415
Maximum91325
Range91311
Interquartile range (IQR)15098

Descriptive statistics

Standard deviation14942.20816
Coefficient of variation (CV)1.035009679
Kurtosis4.600370959
Mean14436.78109
Median Absolute Deviation (MAD)6427
Skewness1.946005205
Sum1148316005
Variance223269584.7
MonotonicityNot monotonic
2025-06-03T16:34:47.234715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
628 5141
 
6.5%
3511 4366
 
5.5%
3510 3977
 
5.0%
6384 2375
 
3.0%
6548 2299
 
2.9%
9846 2005
 
2.5%
12368 1282
 
1.6%
18409 1184
 
1.5%
6560 1177
 
1.5%
6672 1079
 
1.4%
Other values (4567) 54656
68.7%
ValueCountFrequency (%)
14 3
 
< 0.1%
35 8
< 0.1%
39 8
< 0.1%
41 4
< 0.1%
49 1
 
< 0.1%
ValueCountFrequency (%)
91325 15
< 0.1%
91280 5
 
< 0.1%
91005 6
 
< 0.1%
90384 4
 
< 0.1%
90311 7
< 0.1%

CITY
Text

Distinct10354
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:47.718106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length25
Mean length8.863732368
Min length3

Characters and Unicode

Total characters705039
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3716 ?
Unique (%)4.7%

Sample

1st rowBreese
2nd rowCarlyle
3rd rowAviston
4th rowCarlyle
5th rowSaint Peters
ValueCountFrequency (%)
city 2141
 
2.1%
new 1217
 
1.2%
san 1168
 
1.2%
beach 946
 
0.9%
park 786
 
0.8%
fort 755
 
0.7%
lake 722
 
0.7%
york 663
 
0.7%
west 660
 
0.7%
saint 643
 
0.6%
Other values (8480) 91511
90.4%
2025-06-03T16:34:48.432559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 64102
 
9.1%
a 60487
 
8.6%
o 54155
 
7.7%
n 53613
 
7.6%
l 47976
 
6.8%
i 44204
 
6.3%
r 44143
 
6.3%
t 37189
 
5.3%
s 30731
 
4.4%
21671
 
3.1%
Other values (55) 246768
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 705039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 64102
 
9.1%
a 60487
 
8.6%
o 54155
 
7.7%
n 53613
 
7.6%
l 47976
 
6.8%
i 44204
 
6.3%
r 44143
 
6.3%
t 37189
 
5.3%
s 30731
 
4.4%
21671
 
3.1%
Other values (55) 246768
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 705039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 64102
 
9.1%
a 60487
 
8.6%
o 54155
 
7.7%
n 53613
 
7.6%
l 47976
 
6.8%
i 44204
 
6.3%
r 44143
 
6.3%
t 37189
 
5.3%
s 30731
 
4.4%
21671
 
3.1%
Other values (55) 246768
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 705039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 64102
 
9.1%
a 60487
 
8.6%
o 54155
 
7.7%
n 53613
 
7.6%
l 47976
 
6.8%
i 44204
 
6.3%
r 44143
 
6.3%
t 37189
 
5.3%
s 30731
 
4.4%
21671
 
3.1%
Other values (55) 246768
35.0%

COUNTY
Text

Distinct1914
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:48.817731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length19
Mean length7.448605768
Min length3

Characters and Unicode

Total characters592477
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)0.2%

Sample

1st rowClinton
2nd rowClinton
3rd rowClinton
4th rowClinton
5th rowSt. Charles
ValueCountFrequency (%)
los 1471
 
1.6%
angeles 1466
 
1.6%
san 1355
 
1.5%
cook 1162
 
1.3%
st 1083
 
1.2%
new 1067
 
1.2%
orange 977
 
1.1%
montgomery 973
 
1.1%
jefferson 947
 
1.0%
harris 888
 
1.0%
Other values (1927) 78951
87.4%
2025-06-03T16:34:49.311233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 60033
 
10.1%
e 55815
 
9.4%
n 46011
 
7.8%
o 45147
 
7.6%
r 38114
 
6.4%
l 33275
 
5.6%
i 32248
 
5.4%
s 28551
 
4.8%
t 24319
 
4.1%
u 15271
 
2.6%
Other values (47) 213693
36.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 592477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 60033
 
10.1%
e 55815
 
9.4%
n 46011
 
7.8%
o 45147
 
7.6%
r 38114
 
6.4%
l 33275
 
5.6%
i 32248
 
5.4%
s 28551
 
4.8%
t 24319
 
4.1%
u 15271
 
2.6%
Other values (47) 213693
36.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 592477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 60033
 
10.1%
e 55815
 
9.4%
n 46011
 
7.8%
o 45147
 
7.6%
r 38114
 
6.4%
l 33275
 
5.6%
i 32248
 
5.4%
s 28551
 
4.8%
t 24319
 
4.1%
u 15271
 
2.6%
Other values (47) 213693
36.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 592477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 60033
 
10.1%
e 55815
 
9.4%
n 46011
 
7.8%
o 45147
 
7.6%
r 38114
 
6.4%
l 33275
 
5.6%
i 32248
 
5.4%
s 28551
 
4.8%
t 24319
 
4.1%
u 15271
 
2.6%
Other values (47) 213693
36.1%

CSA
Text

Missing 

Distinct155
Distinct (%)0.3%
Missing31357
Missing (%)39.4%
Memory size621.6 KiB
2025-06-03T16:34:49.564056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length58
Median length41
Mean length31.90851925
Min length3

Characters and Unicode

Total characters1537512
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSt. Louis-St. Charles-Farmington, MO-IL
2nd rowSt. Louis-St. Charles-Farmington, MO-IL
3rd rowSt. Louis-St. Charles-Farmington, MO-IL
4th rowSt. Louis-St. Charles-Farmington, MO-IL
5th rowSt. Louis-St. Charles-Farmington, MO-IL
ValueCountFrequency (%)
new 5058
 
3.9%
ny-nj-ct-pa 4661
 
3.6%
york-newark 4661
 
3.6%
fl 2896
 
2.2%
tx 2639
 
2.0%
il-in-wi 2386
 
1.8%
chicago-naperville 2386
 
1.8%
st 2382
 
1.8%
boston-worcester-providence 2212
 
1.7%
ma-ri-nh-ct 2212
 
1.7%
Other values (294) 99105
75.9%
2025-06-03T16:34:49.970862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 132236
 
8.6%
a 99827
 
6.5%
e 99057
 
6.4%
o 84261
 
5.5%
n 83220
 
5.4%
82413
 
5.4%
r 76193
 
5.0%
i 66479
 
4.3%
t 64955
 
4.2%
l 63105
 
4.1%
Other values (46) 685766
44.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1537512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 132236
 
8.6%
a 99827
 
6.5%
e 99057
 
6.4%
o 84261
 
5.5%
n 83220
 
5.4%
82413
 
5.4%
r 76193
 
5.0%
i 66479
 
4.3%
t 64955
 
4.2%
l 63105
 
4.1%
Other values (46) 685766
44.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1537512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 132236
 
8.6%
a 99827
 
6.5%
e 99057
 
6.4%
o 84261
 
5.5%
n 83220
 
5.4%
82413
 
5.4%
r 76193
 
5.0%
i 66479
 
4.3%
t 64955
 
4.2%
l 63105
 
4.1%
Other values (46) 685766
44.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1537512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 132236
 
8.6%
a 99827
 
6.5%
e 99057
 
6.4%
o 84261
 
5.5%
n 83220
 
5.4%
82413
 
5.4%
r 76193
 
5.0%
i 66479
 
4.3%
t 64955
 
4.2%
l 63105
 
4.1%
Other values (46) 685766
44.6%

CSA_FLG
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.6057756377
Minimum0
Maximum1
Zeros31357
Zeros (%)39.4%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:50.062900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4886865221
Coefficient of variation (CV)0.8067120757
Kurtosis-1.812635728
Mean0.6057756377
Median Absolute Deviation (MAD)0
Skewness-0.4329085934
Sum48184
Variance0.2388145169
MonotonicityNot monotonic
2025-06-03T16:34:50.148474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1 48184
60.6%
0 31357
39.4%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 31357
39.4%
1 48184
60.6%
ValueCountFrequency (%)
1 48184
60.6%
0 31357
39.4%

CSA_NO
Real number (ℝ)

Missing  Zeros 

Distinct155
Distinct (%)0.2%
Missing11207
Missing (%)14.1%
Infinite0
Infinite (%)0.0%
Mean232.0858564
Minimum0
Maximum566
Zeros20151
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:50.273397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median212
Q3408
95-th percentile534
Maximum566
Range566
Interquartile range (IQR)408

Descriptive statistics

Standard deviation185.2390292
Coefficient of variation (CV)0.79814872
Kurtosis-1.367276164
Mean232.0858564
Median Absolute Deviation (MAD)196
Skewness0.05511442145
Sum15859587
Variance34313.49796
MonotonicityNot monotonic
2025-06-03T16:34:50.419914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20151
25.3%
408 4661
 
5.9%
176 2386
 
3.0%
148 2212
 
2.8%
548 1965
 
2.5%
206 1797
 
2.3%
428 1661
 
2.1%
370 1515
 
1.9%
288 1407
 
1.8%
122 1232
 
1.5%
Other values (145) 29348
36.9%
(Missing) 11207
 
14.1%
ValueCountFrequency (%)
0 20151
25.3%
104 357
 
0.4%
106 189
 
0.2%
107 61
 
0.1%
108 81
 
0.1%
ValueCountFrequency (%)
566 149
 
0.2%
558 58
 
0.1%
556 236
 
0.3%
554 102
 
0.1%
548 1965
2.5%

ESTYMD
Text

Distinct26127
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:50.756697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.387644263
Min length1

Characters and Unicode

Total characters746712
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13497 ?
Unique (%)17.0%

Sample

1st row30964
2nd row25373
3rd row39104
4th row01/01/1878
5th row38383
ValueCountFrequency (%)
01/01/1889 312
 
0.4%
01/01/1890 249
 
0.3%
01/01/1934 233
 
0.3%
01/01/1919 232
 
0.3%
01/01/1923 232
 
0.3%
01/01/1887 205
 
0.3%
01/01/1921 159
 
0.2%
10/23/1989 156
 
0.2%
06/30/1987 153
 
0.2%
06/30/1985 148
 
0.2%
Other values (26117) 77463
97.4%
2025-06-03T16:34:51.222577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 152117
20.4%
/ 139934
18.7%
1 134962
18.1%
2 81802
11.0%
9 69713
9.3%
8 33477
 
4.5%
3 31566
 
4.2%
7 27990
 
3.7%
6 26864
 
3.6%
5 24649
 
3.3%
Other values (7) 23638
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 746712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 152117
20.4%
/ 139934
18.7%
1 134962
18.1%
2 81802
11.0%
9 69713
9.3%
8 33477
 
4.5%
3 31566
 
4.2%
7 27990
 
3.7%
6 26864
 
3.6%
5 24649
 
3.3%
Other values (7) 23638
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 746712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 152117
20.4%
/ 139934
18.7%
1 134962
18.1%
2 81802
11.0%
9 69713
9.3%
8 33477
 
4.5%
3 31566
 
4.2%
7 27990
 
3.7%
6 26864
 
3.6%
5 24649
 
3.3%
Other values (7) 23638
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 746712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 152117
20.4%
/ 139934
18.7%
1 134962
18.1%
2 81802
11.0%
9 69713
9.3%
8 33477
 
4.5%
3 31566
 
4.2%
7 27990
 
3.7%
6 26864
 
3.6%
5 24649
 
3.3%
Other values (7) 23638
 
3.2%

FI_UNINUM
Real number (ℝ)

Distinct4577
Distinct (%)5.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean32378.41709
Minimum6
Maximum650140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:51.361777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile417
Q12239
median6212
Q312494
95-th percentile82012
Maximum650140
Range650134
Interquartile range (IQR)10255

Descriptive statistics

Standard deviation94083.08362
Coefficient of variation (CV)2.905734501
Kurtosis15.52105594
Mean32378.41709
Median Absolute Deviation (MAD)4088
Skewness4.090154703
Sum2575411674
Variance8851626624
MonotonicityNot monotonic
2025-06-03T16:34:51.516418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
417 5141
 
6.5%
2239 4366
 
5.5%
2238 3977
 
5.0%
4287 2375
 
3.0%
4383 2299
 
2.9%
6300 2005
 
2.5%
7866 1282
 
1.6%
12315 1184
 
1.5%
4390 1177
 
1.5%
4470 1079
 
1.4%
Other values (4567) 54656
68.7%
ValueCountFrequency (%)
6 3
 
< 0.1%
17 8
< 0.1%
21 8
< 0.1%
23 4
< 0.1%
30 1
 
< 0.1%
ValueCountFrequency (%)
650140 1
 
< 0.1%
649963 1
 
< 0.1%
647694 1
 
< 0.1%
645970 1
 
< 0.1%
644984 4
< 0.1%

ID
Real number (ℝ)

Distinct79541
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean305329.7449
Minimum3
Maximum663907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:51.655604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6214
Q1198678
median263437
Q3464262
95-th percentile627297
Maximum663907
Range663904
Interquartile range (IQR)265584

Descriptive statistics

Standard deviation191723.5954
Coefficient of variation (CV)0.6279230853
Kurtosis-0.9537742835
Mean305329.7449
Median Absolute Deviation (MAD)168927
Skewness0.08674045972
Sum2.428623324 × 1010
Variance3.675793705 × 1010
MonotonicityNot monotonic
2025-06-03T16:34:51.809132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
629595 1
 
< 0.1%
223055 1
 
< 0.1%
232078 1
 
< 0.1%
466427 1
 
< 0.1%
9231 1
 
< 0.1%
429739 1
 
< 0.1%
641411 1
 
< 0.1%
641392 1
 
< 0.1%
632193 1
 
< 0.1%
621883 1
 
< 0.1%
Other values (79531) 79531
> 99.9%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
663907 1
< 0.1%
663906 1
< 0.1%
663905 1
< 0.1%
663904 1
< 0.1%
663898 1
< 0.1%

LATITUDE
Real number (ℝ)

Zeros 

Distinct72751
Distinct (%)91.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean35.08090051
Minimum-24.89278582
Maximum71.293282
Zeros5766
Zeros (%)7.2%
Negative1
Negative (%)< 0.1%
Memory size621.6 KiB
2025-06-03T16:34:51.955417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-24.89278582
5-th percentile0
Q133.44270901
median38.32520001
Q341.24813555
95-th percentile44.986226
Maximum71.293282
Range96.18606782
Interquartile range (IQR)7.805426545

Descriptive statistics

Standard deviation11.03089641
Coefficient of variation (CV)0.3144416548
Kurtosis4.645609341
Mean35.08090051
Median Absolute Deviation (MAD)3.797493989
Skewness-2.211858374
Sum2790369.908
Variance121.6806756
MonotonicityNot monotonic
2025-06-03T16:34:52.118624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5766
 
7.2%
38.97345401 11
 
< 0.1%
33.50985786 9
 
< 0.1%
18.210621 8
 
< 0.1%
18.347484 8
 
< 0.1%
18.103866 6
 
< 0.1%
29.42687902 6
 
< 0.1%
44.97609999 6
 
< 0.1%
43.08686201 5
 
< 0.1%
42.934779 5
 
< 0.1%
Other values (72741) 73711
92.7%
ValueCountFrequency (%)
-24.89278582 1
 
< 0.1%
0 5766
7.2%
5.32492 1
 
< 0.1%
6.964 2
 
< 0.1%
7.34391 1
 
< 0.1%
ValueCountFrequency (%)
71.293282 1
< 0.1%
66.892278 1
< 0.1%
66.56444 1
< 0.1%
64.85844899 1
< 0.1%
64.85833001 1
< 0.1%

LONGITUDE
Real number (ℝ)

Zeros 

Distinct72774
Distinct (%)91.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-83.77563019
Minimum-166.2485582
Maximum163.01083
Zeros5766
Zeros (%)7.2%
Negative73745
Negative (%)92.7%
Memory size621.6 KiB
2025-06-03T16:34:52.258200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-166.2485582
5-th percentile-121.1655976
Q1-95.93160797
median-85.90068998
Q3-77.36972078
95-th percentile0
Maximum163.01083
Range329.2593882
Interquartile range (IQR)18.56188719

Descriptive statistics

Standard deviation27.77400847
Coefficient of variation (CV)-0.3315284935
Kurtosis5.258144479
Mean-83.77563019
Median Absolute Deviation (MAD)9.340964979
Skewness1.848770714
Sum-6663597.401
Variance771.3955462
MonotonicityNot monotonic
2025-06-03T16:34:52.411339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5766
 
7.2%
-92.296582 11
 
< 0.1%
-112.0348945 9
 
< 0.1%
-66.028075 8
 
< 0.1%
-66.059463 8
 
< 0.1%
-98.495664 6
 
< 0.1%
-66.625687 6
 
< 0.1%
-93.27205096 5
 
< 0.1%
-85.51531002 5
 
< 0.1%
-85.49964299 5
 
< 0.1%
Other values (72764) 73712
92.7%
ValueCountFrequency (%)
-166.2485582 1
< 0.1%
-165.4092107 1
< 0.1%
-165.4082716 1
< 0.1%
-162.592615 1
< 0.1%
-161.759877 1
< 0.1%
ValueCountFrequency (%)
163.01083 1
< 0.1%
158.2056 2
< 0.1%
151.84316 1
< 0.1%
151.84113 1
< 0.1%
145.760035 1
< 0.1%

MAINOFF
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.05754265096
Minimum0
Maximum1
Zeros74964
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:52.525971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2328780284
Coefficient of variation (CV)4.047050744
Kurtosis12.44032721
Mean0.05754265096
Median Absolute Deviation (MAD)0
Skewness3.800001895
Sum4577
Variance0.05423217609
MonotonicityNot monotonic
2025-06-03T16:34:52.613783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 74964
94.2%
1 4577
 
5.8%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 74964
94.2%
1 4577
 
5.8%
ValueCountFrequency (%)
1 4577
 
5.8%
0 74964
94.2%

MDI_STATUS_CODE
Unsupported

Missing  Rejected  Unsupported 

Missing77963
Missing (%)98.0%
Memory size621.6 KiB

MDI_STATUS_DESC
Text

Missing 

Distinct11
Distinct (%)0.7%
Missing77963
Missing (%)98.0%
Memory size621.6 KiB
2025-06-03T16:34:52.786399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length62
Median length53
Mean length41.64471184
Min length4

Characters and Unicode

Total characters65757
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowASIAN OR PACIFIC ISLANDER AMERICANS
2nd rowASIAN OR PACIFIC ISLANDER AMERICANS
3rd rowASIAN OR PACIFIC ISLANDER AMERICANS
4th rowASIAN OR PACIFIC ISLANDER AMERICANS
5th rowASIAN OR PACIFIC ISLANDER AMERICANS
ValueCountFrequency (%)
minority 844
9.2%
board 844
9.2%
and 844
9.2%
serving 844
9.2%
community 844
9.2%
or 824
9.0%
pacific 719
7.9%
asian 719
7.9%
islander 719
7.9%
hispanic 644
7.1%
Other values (8) 1280
14.0%
2025-06-03T16:34:53.080680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 8713
13.3%
A 7650
11.6%
7546
11.5%
N 6739
10.2%
R 5033
 
7.7%
C 3885
 
5.9%
S 3443
 
5.2%
M 3391
 
5.2%
O 3362
 
5.1%
E 2634
 
4.0%
Other values (14) 13361
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 8713
13.3%
A 7650
11.6%
7546
11.5%
N 6739
10.2%
R 5033
 
7.7%
C 3885
 
5.9%
S 3443
 
5.2%
M 3391
 
5.2%
O 3362
 
5.1%
E 2634
 
4.0%
Other values (14) 13361
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 8713
13.3%
A 7650
11.6%
7546
11.5%
N 6739
10.2%
R 5033
 
7.7%
C 3885
 
5.9%
S 3443
 
5.2%
M 3391
 
5.2%
O 3362
 
5.1%
E 2634
 
4.0%
Other values (14) 13361
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 8713
13.3%
A 7650
11.6%
7546
11.5%
N 6739
10.2%
R 5033
 
7.7%
C 3885
 
5.9%
S 3443
 
5.2%
M 3391
 
5.2%
O 3362
 
5.1%
E 2634
 
4.0%
Other values (14) 13361
20.3%

NAME
Text

Distinct4016
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:53.298977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length68
Median length56
Mean length25.62086696
Min length3

Characters and Unicode

Total characters2037935
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique704 ?
Unique (%)0.9%

Sample

1st row1NB Bank
2nd row1NB Bank
3rd row1NB Bank
4th row1NB Bank
5th row1st Advantage Bank
ValueCountFrequency (%)
bank 72321
24.8%
national 35733
 
12.2%
association 28951
 
9.9%
of 9981
 
3.4%
first 6776
 
2.3%
trust 6207
 
2.1%
the 5258
 
1.8%
chase 5142
 
1.8%
jpmorgan 5142
 
1.8%
wells 4375
 
1.5%
Other values (2681) 112192
38.4%
2025-06-03T16:34:54.137869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 247526
12.1%
212925
 
10.4%
n 202241
 
9.9%
i 151721
 
7.4%
o 150523
 
7.4%
t 125249
 
6.1%
s 112556
 
5.5%
B 81439
 
4.0%
k 78621
 
3.9%
e 72465
 
3.6%
Other values (61) 602669
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2037935
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 247526
12.1%
212925
 
10.4%
n 202241
 
9.9%
i 151721
 
7.4%
o 150523
 
7.4%
t 125249
 
6.1%
s 112556
 
5.5%
B 81439
 
4.0%
k 78621
 
3.9%
e 72465
 
3.6%
Other values (61) 602669
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2037935
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 247526
12.1%
212925
 
10.4%
n 202241
 
9.9%
i 151721
 
7.4%
o 150523
 
7.4%
t 125249
 
6.1%
s 112556
 
5.5%
B 81439
 
4.0%
k 78621
 
3.9%
e 72465
 
3.6%
Other values (61) 602669
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2037935
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 247526
12.1%
212925
 
10.4%
n 202241
 
9.9%
i 151721
 
7.4%
o 150523
 
7.4%
t 125249
 
6.1%
s 112556
 
5.5%
B 81439
 
4.0%
k 78621
 
3.9%
e 72465
 
3.6%
Other values (61) 602669
29.6%

OFFNAME
Text

Distinct51238
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:54.577876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length93
Median length69
Mean length20.07865027
Min length3

Characters and Unicode

Total characters1597096
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42175 ?
Unique (%)53.0%

Sample

1st rowST. ROSE FACILITY BRANCH
2nd row1350 12TH STREET FACILITY
3rd rowAVISTON BRANCH
4th row1NB Bank
5th row1st Advantage Bank
ValueCountFrequency (%)
branch 69727
29.2%
bank 6361
 
2.7%
3614
 
1.5%
center 2868
 
1.2%
street 2211
 
0.9%
main 2039
 
0.9%
and 1917
 
0.8%
office 1871
 
0.8%
west 1787
 
0.7%
banking 1755
 
0.7%
Other values (18828) 144855
60.6%
2025-06-03T16:34:55.202850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
160059
 
10.0%
A 135262
 
8.5%
N 127057
 
8.0%
R 120499
 
7.5%
B 93985
 
5.9%
C 91654
 
5.7%
H 84757
 
5.3%
E 82327
 
5.2%
O 57740
 
3.6%
T 53707
 
3.4%
Other values (71) 590049
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1597096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
160059
 
10.0%
A 135262
 
8.5%
N 127057
 
8.0%
R 120499
 
7.5%
B 93985
 
5.9%
C 91654
 
5.7%
H 84757
 
5.3%
E 82327
 
5.2%
O 57740
 
3.6%
T 53707
 
3.4%
Other values (71) 590049
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1597096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
160059
 
10.0%
A 135262
 
8.5%
N 127057
 
8.0%
R 120499
 
7.5%
B 93985
 
5.9%
C 91654
 
5.7%
H 84757
 
5.3%
E 82327
 
5.2%
O 57740
 
3.6%
T 53707
 
3.4%
Other values (71) 590049
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1597096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
160059
 
10.0%
A 135262
 
8.5%
N 127057
 
8.0%
R 120499
 
7.5%
B 93985
 
5.9%
C 91654
 
5.7%
H 84757
 
5.3%
E 82327
 
5.2%
O 57740
 
3.6%
T 53707
 
3.4%
Other values (71) 590049
36.9%

OFFNUM
Real number (ℝ)

Zeros 

Distinct9708
Distinct (%)12.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1410.449994
Minimum0
Maximum10535
Zeros4577
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:55.349972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median143
Q31616
95-th percentile7774
Maximum10535
Range10535
Interquartile range (IQR)1605

Descriptive statistics

Standard deviation2442.182406
Coefficient of variation (CV)1.731491662
Kurtosis3.062917653
Mean1410.449994
Median Absolute Deviation (MAD)142
Skewness2.007525141
Sum112188603
Variance5964254.902
MonotonicityNot monotonic
2025-06-03T16:34:55.493720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4577
 
5.8%
1 2512
 
3.2%
2 2319
 
2.9%
3 1959
 
2.5%
4 1689
 
2.1%
5 1471
 
1.8%
6 1282
 
1.6%
7 1123
 
1.4%
8 998
 
1.3%
9 897
 
1.1%
Other values (9698) 60714
76.3%
ValueCountFrequency (%)
0 4577
5.8%
1 2512
3.2%
2 2319
2.9%
3 1959
2.5%
4 1689
 
2.1%
ValueCountFrequency (%)
10535 1
< 0.1%
10534 1
< 0.1%
10533 1
< 0.1%
10532 1
< 0.1%
10531 1
< 0.1%

RUNDATE
Real number (ℝ)

Constant 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean45401
Minimum45401
Maximum45401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:55.608483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum45401
5-th percentile45401
Q145401
median45401
Q345401
95-th percentile45401
Maximum45401
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean45401
Median Absolute Deviation (MAD)0
Skewness0
Sum3611240941
Variance0
MonotonicityIncreasing
2025-06-03T16:34:55.691760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
45401 79541
> 99.9%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
45401 79541
> 99.9%
ValueCountFrequency (%)
45401 79541
> 99.9%

SERVTYPE
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11.73479086
Minimum11
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:55.784584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median11
Q311
95-th percentile13
Maximum99
Range88
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.65520907
Coefficient of variation (CV)0.3114848073
Kurtosis210.1744611
Mean11.73479086
Median Absolute Deviation (MAD)0
Skewness10.82671246
Sum933397
Variance13.36055335
MonotonicityNot monotonic
2025-06-03T16:34:55.879905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
11 72967
91.7%
12 2575
 
3.2%
23 1537
 
1.9%
24 892
 
1.1%
29 359
 
0.5%
27 310
 
0.4%
21 249
 
0.3%
30 178
 
0.2%
13 177
 
0.2%
26 154
 
0.2%
Other values (4) 143
 
0.2%
ValueCountFrequency (%)
11 72967
91.7%
12 2575
 
3.2%
13 177
 
0.2%
21 249
 
0.3%
22 4
 
< 0.1%
ValueCountFrequency (%)
99 50
 
0.1%
30 178
0.2%
29 359
0.5%
28 61
 
0.1%
27 310
0.4%

SERVTYPE_DESC
Text

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:56.042171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length31
Mean length30.95352141
Min length13

Characters and Unicode

Total characters2462105
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFULL SERVICE - BRICK AND MORTAR
2nd rowFULL SERVICE - BRICK AND MORTAR
3rd rowFULL SERVICE - BRICK AND MORTAR
4th rowFULL SERVICE - BRICK AND MORTAR
5th rowFULL SERVICE - BRICK AND MORTAR
ValueCountFrequency (%)
service 79541
17.0%
79541
17.0%
full 75719
16.2%
brick 72967
15.6%
and 72967
15.6%
mortar 72967
15.6%
limited 3822
 
0.8%
retail 2636
 
0.6%
facility 1541
 
0.3%
drive 1537
 
0.3%
Other values (16) 5086
 
1.1%
2025-06-03T16:34:56.327240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
388782
15.8%
R 306096
12.4%
E 172384
 
7.0%
I 169668
 
6.9%
L 161205
 
6.5%
C 156943
 
6.4%
A 154295
 
6.3%
T 86177
 
3.5%
D 82570
 
3.4%
S 81336
 
3.3%
Other values (15) 702649
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2462105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
388782
15.8%
R 306096
12.4%
E 172384
 
7.0%
I 169668
 
6.9%
L 161205
 
6.5%
C 156943
 
6.4%
A 154295
 
6.3%
T 86177
 
3.5%
D 82570
 
3.4%
S 81336
 
3.3%
Other values (15) 702649
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2462105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
388782
15.8%
R 306096
12.4%
E 172384
 
7.0%
I 169668
 
6.9%
L 161205
 
6.5%
C 156943
 
6.4%
A 154295
 
6.3%
T 86177
 
3.5%
D 82570
 
3.4%
S 81336
 
3.3%
Other values (15) 702649
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2462105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
388782
15.8%
R 306096
12.4%
E 172384
 
7.0%
I 169668
 
6.9%
L 161205
 
6.5%
C 156943
 
6.4%
A 154295
 
6.3%
T 86177
 
3.5%
D 82570
 
3.4%
S 81336
 
3.3%
Other values (15) 702649
28.5%

STALP
Text

Missing 

Distinct59
Distinct (%)0.1%
Missing803
Missing (%)1.0%
Memory size621.6 KiB
2025-06-03T16:34:56.538343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.000038101
Min length2

Characters and Unicode

Total characters157481
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIL
2nd rowIL
3rd rowIL
4th rowIL
5th rowMO
ValueCountFrequency (%)
tx 6359
 
8.1%
ca 5770
 
7.3%
fl 4349
 
5.5%
ny 4151
 
5.3%
il 3721
 
4.7%
pa 3473
 
4.4%
oh 3127
 
4.0%
nj 2350
 
3.0%
mi 2177
 
2.8%
mo 2153
 
2.7%
Other values (49) 41109
52.2%
2025-06-03T16:34:56.891994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 23243
14.8%
N 16661
10.6%
I 11738
 
7.5%
C 11560
 
7.3%
M 11436
 
7.3%
L 10823
 
6.9%
T 10498
 
6.7%
O 8743
 
5.6%
X 6359
 
4.0%
Y 5881
 
3.7%
Other values (14) 40539
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 157481
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 23243
14.8%
N 16661
10.6%
I 11738
 
7.5%
C 11560
 
7.3%
M 11436
 
7.3%
L 10823
 
6.9%
T 10498
 
6.7%
O 8743
 
5.6%
X 6359
 
4.0%
Y 5881
 
3.7%
Other values (14) 40539
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 157481
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 23243
14.8%
N 16661
10.6%
I 11738
 
7.5%
C 11560
 
7.3%
M 11436
 
7.3%
L 10823
 
6.9%
T 10498
 
6.7%
O 8743
 
5.6%
X 6359
 
4.0%
Y 5881
 
3.7%
Other values (14) 40539
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 157481
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 23243
14.8%
N 16661
10.6%
I 11738
 
7.5%
C 11560
 
7.3%
M 11436
 
7.3%
L 10823
 
6.9%
T 10498
 
6.7%
O 8743
 
5.6%
X 6359
 
4.0%
Y 5881
 
3.7%
Other values (14) 40539
25.7%

STCNTY
Real number (ℝ)

Zeros 

Distinct3198
Distinct (%)4.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean28538.93975
Minimum0
Maximum78030
Zeros803
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:57.016426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5085
Q116001
median28115
Q342017
95-th percentile53031
Maximum78030
Range78030
Interquartile range (IQR)26016

Descriptive statistics

Standard deviation15864.22227
Coefficient of variation (CV)0.5558798751
Kurtosis-1.074952393
Mean28538.93975
Median Absolute Deviation (MAD)13894
Skewness0.009780419727
Sum2270015807
Variance251673548.2
MonotonicityNot monotonic
2025-06-03T16:34:57.165195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6037 1466
 
1.8%
17031 1155
 
1.5%
48201 885
 
1.1%
0 803
 
1.0%
4013 666
 
0.8%
48113 597
 
0.8%
12086 581
 
0.7%
6059 557
 
0.7%
36061 551
 
0.7%
6073 469
 
0.6%
Other values (3188) 71811
90.3%
ValueCountFrequency (%)
0 803
1.0%
1001 15
 
< 0.1%
1003 89
 
0.1%
1005 10
 
< 0.1%
1007 7
 
< 0.1%
ValueCountFrequency (%)
78030 9
< 0.1%
78020 2
 
< 0.1%
78010 9
< 0.1%
72153 4
< 0.1%
72151 2
 
< 0.1%

STNAME
Text

Missing 

Distinct59
Distinct (%)0.1%
Missing803
Missing (%)1.0%
Memory size621.6 KiB
2025-06-03T16:34:57.432226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length24
Mean length8.373969697
Min length4

Characters and Unicode

Total characters659358
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIllinois
2nd rowIllinois
3rd rowIllinois
4th rowIllinois
5th rowMissouri
ValueCountFrequency (%)
new 7309
 
8.0%
texas 6359
 
6.9%
california 5770
 
6.3%
florida 4349
 
4.7%
york 4151
 
4.5%
illinois 3721
 
4.1%
pennsylvania 3473
 
3.8%
carolina 3216
 
3.5%
ohio 3127
 
3.4%
north 2456
 
2.7%
Other values (60) 47765
52.1%
2025-06-03T16:34:57.822243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 82914
12.6%
i 71373
 
10.8%
n 57355
 
8.7%
s 52334
 
7.9%
o 51967
 
7.9%
e 43409
 
6.6%
r 37085
 
5.6%
l 30365
 
4.6%
t 17740
 
2.7%
h 15700
 
2.4%
Other values (38) 199116
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 659358
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 82914
12.6%
i 71373
 
10.8%
n 57355
 
8.7%
s 52334
 
7.9%
o 51967
 
7.9%
e 43409
 
6.6%
r 37085
 
5.6%
l 30365
 
4.6%
t 17740
 
2.7%
h 15700
 
2.4%
Other values (38) 199116
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 659358
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 82914
12.6%
i 71373
 
10.8%
n 57355
 
8.7%
s 52334
 
7.9%
o 51967
 
7.9%
e 43409
 
6.6%
r 37085
 
5.6%
l 30365
 
4.6%
t 17740
 
2.7%
h 15700
 
2.4%
Other values (38) 199116
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 659358
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 82914
12.6%
i 71373
 
10.8%
n 57355
 
8.7%
s 52334
 
7.9%
o 51967
 
7.9%
e 43409
 
6.6%
r 37085
 
5.6%
l 30365
 
4.6%
t 17740
 
2.7%
h 15700
 
2.4%
Other values (38) 199116
30.2%

UNINUM
Real number (ℝ)

Distinct79541
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean305329.7449
Minimum3
Maximum663907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:57.957682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6214
Q1198678
median263437
Q3464262
95-th percentile627297
Maximum663907
Range663904
Interquartile range (IQR)265584

Descriptive statistics

Standard deviation191723.5954
Coefficient of variation (CV)0.6279230853
Kurtosis-0.9537742835
Mean305329.7449
Median Absolute Deviation (MAD)168927
Skewness0.08674045972
Sum2.428623324 × 1010
Variance3.675793705 × 1010
MonotonicityNot monotonic
2025-06-03T16:34:58.098514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
629595 1
 
< 0.1%
223055 1
 
< 0.1%
232078 1
 
< 0.1%
466427 1
 
< 0.1%
9231 1
 
< 0.1%
429739 1
 
< 0.1%
641411 1
 
< 0.1%
641392 1
 
< 0.1%
632193 1
 
< 0.1%
621883 1
 
< 0.1%
Other values (79531) 79531
> 99.9%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
663907 1
< 0.1%
663906 1
< 0.1%
663905 1
< 0.1%
663904 1
< 0.1%
663898 1
< 0.1%

ZIP
Real number (ℝ)

Distinct18284
Distinct (%)23.0%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean48949.24647
Minimum0
Maximum99929
Zeros794
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size621.6 KiB
2025-06-03T16:34:58.252729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4602.3
Q127540
median47715
Q373529
95-th percentile94591
Maximum99929
Range99929
Interquartile range (IQR)45989

Descriptive statistics

Standard deviation28237.23087
Coefficient of variation (CV)0.5768675292
Kurtosis-1.118607615
Mean48949.24647
Median Absolute Deviation (MAD)24111
Skewness0.04158900018
Sum3893374115
Variance797341207.1
MonotonicityNot monotonic
2025-06-03T16:34:58.404973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 794
 
1.0%
10022 56
 
0.1%
33134 47
 
0.1%
75225 46
 
0.1%
85016 43
 
0.1%
33131 43
 
0.1%
72401 41
 
0.1%
37027 41
 
0.1%
17601 40
 
0.1%
76107 40
 
0.1%
Other values (18274) 78348
98.5%
ValueCountFrequency (%)
0 794
1.0%
100 1
 
< 0.1%
601 1
 
< 0.1%
602 2
 
< 0.1%
603 4
 
< 0.1%
ValueCountFrequency (%)
99929 2
 
< 0.1%
99921 2
 
< 0.1%
99901 6
< 0.1%
99840 1
 
< 0.1%
99835 4
< 0.1%